论文标题
在应用程序的各个领域中用于对象检测的最新模型
State-of-the-art Models for Object Detection in Various Fields of Application
论文作者
论文摘要
我们提出了数据集及其最佳模型列表，目的是通过将对象识别问题放在两种类型的最新方法的上下文中：一个阶段方法和两个阶段方法。我们根据砂油深度学习模型的最新发展（可可零售，可可测试，Pascal VOC 2007，ADE20K和Imagenet）提供了对五个顶级数据集的深入统计分析。在将数据质量，数据质量，最小偏差，标签质量等方面对其剩余的质量进行了紧密比较之后，将这些数据集进行了精心挑选。更重要的是，我们的工作扩展到了过去两年中这些数据集的最佳组合与新兴模型的最佳组合。它列出了每个数据集的顶级模型及其最佳用例。我们已经提供了各种通用对象检测模型和特定对象检测模型的全面概述，并获得了比较结果，例如在不同的联合（IOUS）和不同尺寸的对象上固定的推理时间和平均精度（AP）。定性和定量分析将允许专家使用数据集和模型的最佳组合来实现新的绩效记录。
We present a list of datasets and their best models with the goal of advancing the state-of-the-art in object detection by placing the question of object recognition in the context of the two types of state-of-the-art methods: one-stage methods and two stage-methods. We provided an in-depth statistical analysis of the five top datasets in the light of recent developments in granulated Deep Learning models - COCO minival, COCO test, Pascal VOC 2007, ADE20K, and ImageNet. The datasets are handpicked after closely comparing them with the rest in terms of diversity, quality of data, minimal bias, labeling quality etc. More importantly, our work extends to provide the best combination of these datasets with the emerging models in the last two years. It lists the top models and their optimal use cases for each of the respective datasets. We have provided a comprehensive overview of a variety of both generic and specific object detection models, enlisting comparative results like inference time and average precision of box (AP) fixed at different Intersection Over Union (IoUs) and for different sized objects. The qualitative and quantitative analysis will allow experts to achieve new performance records using the best combination of datasets and models.
